未來展望

1. 在本研究中我們針對歸屬函數的設定進行基因演算法的修正與增強式學 習法則的修正，使系統輸出性能響應達到最佳化的目的。然而，模糊控 制之模糊規則一樣會對整體控制造成影響，在未來也可以嘗試將模糊規 則以染色體表示，運用基因演算法演化找出最佳解。

2. 在本研究中針對模糊變數的輸入參數採用距離與角度二個變數，雖然模 擬的結果與實作的驗證均顯示可以達到控制目的，但是某些輸入變數在 本研究中並未加以考慮，例如：加速度、角加速度…等。未來將嘗試增 加模糊控制的輸入歸屬函數，以增加控制的穩定度，也增加使用基因演 算法結合增強式學習尋找最佳解的實用性。

3. 在本研究中基於運算性能的考量所使用之增強式學習法則的修正動作略 顯簡略。未來可以在增強式學習階段加入更多修改的動作與狀態，使修 正的效果更明顯，也更趨近於最佳解。然而，精確度與運算速度之間的 取捨問題(trade off)將可成為未來繼續深入研究的方向之一。

4. 未來將嘗試加入遞增式(incremental)基因演算法，將依據實體機器人拾取 目標物件效能調整模擬器中決定適應度的權重參數(,, )。透過一次又 一次的實機測試，將漸漸調整出最適合的參數，讓模擬的機器人行為最 接近於實機測試的結果。

5. 未來也將模擬器基因演算法的部分用鳥群演算法取代，並比較其收斂結 果與實作在實體機器人上的成效。

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